IFT3335, Winter 2022

Artificial Intelligence: An Introduction



Time and Place

Wednesday 12:30pm - 13:30pm
Online via Zoom

Wednesday (Practice Work) 13:30pm - 15:30pm
Online via Zoom

Thursday 9:30am - 11:30am
Online via Zoom

Please check the Studium platform for more information about this course.

Instructor

Bang Liu
Office : 2221
Email: firstname.lastname@umontreal.ca

Jian-Yun Nie
Email: lastname@iro.umontreal.ca

Course description

This is an introductory level course to Artificial Intelligence. In this course, students will gain a thorough introduction to the basics of AI. We will introduce searching algorithms, knowledge, reasoning and planning, probabilistic reasoning, machine learning, deep learning, and natural language processing.

Prerequisites

  • Proficiency in Python
    All programming assignments will be in Python. If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Java/Javascript), you will probably be fine.
  • Basic Probability and Statistics
    You should know the basics of probabilities, gaussian distributions, mean, standard deviation, etc.

Reference Texts

No textbook is required. But the following texts that can be read free online are helpful.

Tentative Time Table

Note: tentative schedule is subject to change.
Date Topic
Section I: Introduction
Lecture 1 Introduction to AI (Slides)
Lecture 2 Agents (Slides)
Section II: Search and Games
Lecture 3 Uninformed Search (Slides)
Lecture 4 Informed Search (Slides)
Lecture 5 Adversarial Search and Games (Slides)
Section III: Logic and Knowledge Representation
Lecture 6 Propositional Logic (Slides)
Lecture 7 First Order Logic (Slides)
Lecture 8 Knowledge Graph (Slides)
Section IV: Uncertainty
Lecture 9 Probabilistic Reasoning (Slides)
Lecture 10 Bayesian Network (Slides)
Section V: Machine Learning
Lecture 11~12 Online Mooc: Techniques d'intelligence artificielle: des fondements aux applications
Section VI: Natural Language Processing
Lecture 13 NLP part 1 (Slides)
Lecture 14 NLP part 2 (Slides)